1. Field of the Invention
The present invention relates to an image processing apparatus and an image processing method.
2. Description of Related Art
As a method of expressing halftones during image formation, a FM (frequency modulation) screen has been conventionally used. As methods of implementing the FM screen, a method by threshold processing using a dither matrix and a method by error diffusion have been known. When using the threshold processing, a dither matrix having a very large size, for example 256 pixels×256 pixels, is necessary for generating random patterns, and a large amount of memory is necessary for storing the dither matrix. Thus, the method using error diffusion processing is more practical in terms of cost.
When considering a relationship between a pattern of the FM screen and an output device, a pattern such as a green noise pattern in which dot dispersibility is low is desirable for an output device in which dot stability of electrophotography or the like is low. The green noise pattern is a pattern including a lot of intermediate frequency components and by which each dot is prevented from being isolated so that continuous dots are formed.
For example, Japanese Patent Application Laid-Open Publication No. 2008-219291 proposes to execute a feedback calculation processing for generating a green noise pattern in an image processing apparatus which binarizes multi-valued image data by the error diffusion method. The image processing apparatus executes a calculation with reference to output data of surrounding pixels to generate the green noise pattern.
A conventional image processing apparatus 1001 will be described with reference to FIG. 12.
The image processing apparatus 1001 includes a binarization section 1010, a green noise calculation section 1011, an adder 1012, a subtractor 1013, an error diffusion section 1014, and a subtractor 1015. The image processing apparatus 1001 performs various kinds of processings to the input multi-valued data to output binary data.
The binarization section 1010 binarizes multi-valued data in each pixel constituting an image based on a predetermined threshold TH0. More specifically, the binarization section 1010 converts multi-valued data to a maximum value (255 for 256 levels of gradation) when the multi-valued data is equal to or more than the threshold TH0 and to a minimum value (0) when the multi-valued data is less than the threshold TH0.
The green noise calculation section 1011 calculates, with respect to each of one or more processed pixels adjacent to a target value, a value obtained by multiplying binary data obtained by binarization of the binarization section 1010 by a weighting coefficient predetermined for each positional relationship between the target pixel and each of the processed pixels.
The adder 1012 adds each of one or more values calculated by the green noise calculation section 1011 to multi-valued data of the target pixel before binarization to output obtained results to the binarization section 1010.
By the green noise calculation section 1011 and the adder 1012, when the binary data of each of the processed pixels has a maximum value (dot-on), the target pixel is also likely to have a maximum value (dot-on) during binarization. In this manner, a green noise pattern is generated.
The subtractor 1013 subtracts multi-valued data of the target pixel before addition by the adder 1012 from binary data obtained by binarizing the target pixel by the binarization section 1010 to output an obtained result to the error diffusion section 1014.
The error diffusion section 1014 calculates each value obtained by multiplying a value output from the subtractor 1013 by the weighting coefficient predetermined for each positional relationship between the target pixel and one or more unprocessed pixels adjacent to the target pixel to output obtained values to the subtractor 1015.
The subtractor 1015 subtracts each of the values calculated by the error diffusion section 1014 for each of the unprocessed pixels from multi-valued data of each of the unprocessed pixels.
FIG. 13A shows an example of a screen pattern which is processed by the image processing apparatus 1001. FIG. 13A shows a gradation whose density decreases from left to right.